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Automated Analysis for Validating STR Data: Enhancing Forensic DNA Analysis

Learn about the validation studies on automated STR data analysis methods, including TrueAllele™ technology, to improve quality, throughput, and efficiency. Explore innovative processes and tools for casework studies, database integrity, and eliminating human review bottlenecks. Discover how automation enhances data processing, quality assurance, and reporting accuracy in forensic science.

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Automated Analysis for Validating STR Data: Enhancing Forensic DNA Analysis

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  1. Automated STR Data Analysis:Validation Studies Mark W. Perlin (Cybergenetics, Pittsburgh, PA) David Coffman (Florida Department of Law Enforcement, Tallahassee, FL) Cecelia A. Crouse & Felipe Konotop (Palm Beach County Sheriff’s Office, FL) Jeffrey D. Ban (Division of Forensic Science, Richmond, VA) Automated Analysis Databasing Validation Casework Studies NIJ grants 2000-IJ-CX-K005 & 2001-IJ-CX-K003

  2. Reviewing STR Data Human data review bottleneck Computer Automation Quality Assurance Database Integrity Casework & Mixtures Key goals: • no error • high throughput • small staff

  3. Fully Automated (on Mac/PC/Unix) Color Separation Image Processing Lane Tracking Signal Analysis Ladder Building Peak Quantification Allele Designation Quality Checking CODIS Reporting TrueAllele™ Technology Eliminates STR human review bottleneck Gel-based, or Sequencer, or Capillary Raw STR Data INPUT Quality Assured Profiles Database OUTPUT Protected by US patents 5,541,067 & 5,580,728 & 5,876,933 & 6,054,268

  4. 1.Input 2.Gel/CE 3.Allele 4.Output Automated Processing

  5. Quality Assurance

  6. Rule System Good data. “Low” optical density? User sets criteria. No need to review. No rules fired.

  7. MegaBACE ABI/3100 plate Multi-Platform Engine

  8. Validation Methods 1. Obtain original data 2. Process data in TrueAllele ES (auto-setup, process run, Q/A, call alleles, apply rules, check) computer: accept/reject/edit 3. Review all data one person, many computers human: accept/reject/edit 4. Generate results & stats

  9. Rule Settings Extract Amplify Separate Other

  10. Hitachi + PowerPlex Hitachi FM/Bio2 & Promega PowerPlex 1.2 ~8,000 PBSO genotypes reviewed TrueAllele performed all gel & allele processing Computer: ~75%* data, no review needed Human: All these designations correct TrueAllele expert system can eliminate most human review of gel STR data

  11. Hitachi Results Human Review Accept Edit Reject Reject Edit Accept Computer Process non-automation data

  12. ABI/310 + Pro/CoFiler ABI 310 & ProfilerPlus/Cofiler ~24,000 FDLE genotypes reviewed TrueAllele performed all CE & allele processing Computer: ~85% data, no review needed Human: All proper designations correct TrueAllele expert system can eliminate most human review of CE STR data

  13. 310 Results Human Review Accept Edit Reject Reject Edit Accept Computer Process 94 genos/min

  14. ABI/3700 + Pro/CoFiler ABI 3700 & ProfilerPlus/Cofiler ~17,000 FDLE genotypes reviewed TrueAllele performed all CE & allele processing Computer: ~85% data, no review needed Human: All TA/ES designations correct TrueAllele expert system can eliminate most human review of CE-array STR data

  15. 3700 Results Human Review Accept Edit Reject Reject Edit Accept Computer Process 76 genos/min

  16. The UK FSS Experience Generate STR Data TrueAllele expert system scores all STR data and assesses data quality Person reviews a fraction of the data UK National DNA Database

  17. FSS ABI/377 Validation Resources • Data: 22,000 genotypes (SGMplus) • People: 6 reviewers + 6 managers • Time: 8 weeks work + 4 weeks report Components • Peak height correlation (GS vs TA) • Establish baseline height (error-free) • Designation accuracy (human vs TA) • Network/computer environment • QMS documentation Results • Greater yield with TA • No errors on quality data

  18. Casework Studies Nonmixture Mixture Rape Kits Disasters LCN, SNPs M.W. Perlin and B. Szabady, “Linear mixture analysis: a mathematical approach to resolving mixed DNA samples,” Journal of Forensic Sciences, November, 2001.

  19. Statistical Information Prob{STR profile } & peak quants} Degenerate SGM+ alleles (6x6x6x10x...x6)  100,000,000 feasible profiles Compute unknown minor contrib profiles  @30% 1 feasible profile  @10% 100 feasible profiles LMA increases identification power a million-fold; CODIS match

  20. Validation Data Sets Collaborators: Florida, Virginia, New York, FBI, UK, Private Labs Data: synthetic lab mixtures, casework, rape kits, disasters Input: TrueAllele quantitated peaks Studies: comparison, concordance, automated lab processes

  21. 1/10 2/6 Rape Kit: Unknown Suspect Known victim Unknown suspect Florida data (FDLE, PBSO) 9:1, 7:3, 5:5, 3:7, 1:9 ratios Six pairs of samples Lab: 70% victim, 30% unknown Computer: 71%, 29%

  22. Other Mixing Proportions Lab: 90% victim, 10% unknown Computer: 90%, 10% Lab: 50% victim, 50% unknown Computer: 52%, 48%  Lab & review automation

  23. Disaster Data Review

  24. Conclusions • TrueAllele databasing validation • Reduce time, error, staff & cost • Ongoing casework validation • Automate: data review & lab work • Serve: police, courts, society • Objective, comprehensive

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